AI / ML Specialist

Work Role ID: 623  |  Workforce Element: AI / Data

What does this work role do? Designs, develops, and modifies AI applications, tools, and/or other solutions to enable successful accomplishment of mission objectives.

KSAT ID Description KSAT
22 * Knowledge of computer networking concepts and protocols, and network security methodologies. Knowledge
108 * Knowledge of risk management processes (e.g., methods for assessing and mitigating risk). Knowledge
1157 * Knowledge of national and international laws, regulations, policies, and ethics as they relate to cybersecurity. Knowledge
1158 * Knowledge of cybersecurity principles. Knowledge
1159 * Knowledge of cyber threats and vulnerabilities. Knowledge
6900 * Knowledge of specific operational impacts of cybersecurity lapses. Knowledge
6935 * Knowledge of cloud computing service models Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS). Knowledge
6938 * Knowledge of cloud computing deployment models in private, public, and hybrid environment and the difference between on-premises and off-premises environments. Knowledge
KSAT ID Description KSAT
21 Knowledge of computer algorithms. Knowledge
75A Knowledge of mathematics, including logarithms, trigonometry, linear algebra, calculus, statistics, and operational analysis. Knowledge
102 Knowledge of programming language structures and logic. Knowledge
166 Skill in conducting queries and developing algorithms to analyze data structures. Skill
477 Correct errors by making appropriate changes and rechecking the program to ensure desired results are produced. Task
506 Design, develop, and modify software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design. Task
543 Develop secure code and error handling. Task
764 Perform secure programming and identify potential flaws in codes to mitigate vulnerabilities. Task
942 Knowledge of the organization’s core business/mission processes. Knowledge
1000B Ensure that AI design and development activities are properly documented and updated. Task
5120 Conduct hypothesis testing using statistical processes. Task
5925 Use knowledge of business processes to create or recommend AI solutions. Task
5847 Assess and address the limitations of methods to deliver machine learning models. Task
5854 Collaborate with appropriate personnel to address Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data resusability concerns for AI solutions. Task
5858 Conduct AI risk assessments to ensure models and/or other solutions are performing as designed. Task
5859 Consider energy implications (graphical processing unit, tensor processing unit, etc.) when designing AI solutions. Task
5870 Design and develop continuous integration/continuous delivery (CI/CD) in a containerized or other reproducible computing environment to support the machine learning life cycle. Task
5871 Design and develop machine learning models to achieve organizational objectives. Task
5872 Design, develop, and implement AI tools and techniques to achieve organizational objectives. Task
5873 Determine methods and metrics for quantitative and qualitative measurement of AI risks so that sensitivity, specificity, likelihood, confidence levels, and other metrics are identified, documented, and applied. Task
5889 Identify and submit exemplary AI use cases, best practices, failure modes, and risk mitigation strategies, including after-action reports. Task
5893 Implement Responsible AI best practices and standards within AI solutions according to the DoD AI Ethical Principles, Responsible AI Guidelines, and/or any other pertinent laws. Task
5896 Maintain current knowledge of advancements in DoD AI Ethical Principles and Responsible AI. Task
5915 Research the latest machine learning and AI tools, techniques, and best practices. Task
5926 Use models and other methods for evaluating AI performance. Task
5927 Write and document reproducible code. Task
6060 Ability to collect, verify, and validate test data. Ability
6290 Knowledge of how to leverage government research and development centers, think tanks, academic research, and industry systems. Knowledge
6311 Knowledge of machine learning theory and principles. Knowledge
6760 Skill in writing scripts using R, Python, PIG, HIVE, SQL, etc. Skill
7003 Knowledge of AI security risks, threats, and vulnerabilities and potential risk mitigation solutions. Knowledge
7009 Knowledge of coding and scripting in languages that support AI development and use. Knowledge
7011 Knowledge of current AI and machine learning systems design and performance analysis models, algorithms, and tools. Knowledge
7020 Knowledge of DoD AI Ethical Principles (e.g., responsible, equitable, traceable, reliable, and governable). Knowledge
7021 Knowledge of emerging trends and future use cases of AI. Knowledge
7022 Knowledge of how AI adoption can assist developers with service-oriented design. Knowledge
7024 Knowledge of how AI is developed and operated. Knowledge
7025 Knowledge of how AI solutions integrate with cloud or other IT infrastructure. Knowledge
7026 Knowledge of how commercial and federal solutions solve Defense-related data environment and platform challenges. Knowledge
7028 Knowledge of how to automate development, testing, security, and deployment of AI/machine learning-enabled software to the DoD. Knowledge
7029 Knowledge of how to collect, store, and monitor data. Knowledge
7031 Knowledge of how to structure and display data. Knowledge
7032 Knowledge of how to use data to tell a story. Knowledge
7036 Knowledge of laws, regulations, and policies related to AI, data security/privacy, and use of publicly procured data for government. Knowledge
7037 Knowledge of machine learning operations (MLOps) processes and best practices. Knowledge
7038 Knowledge of metrics to evaluate the effectiveness of machine learning models. Knowledge
7040 Knowledge of Personal Health Information (PHI), Personally Identifiable Information (PII), and other data privacy and data reusability considerations for AI solutions. Knowledge
7041 Knowledge of remedies against unintended bias in AI solutions. Knowledge
7044 Knowledge of testing, evaluation, validation, and verification (T&E V&V) tools and procedures to ensure systems are working as intended. Knowledge
7045 Knowledge of the AI lifecycle. Knowledge
7046 Knowledge of the basic requirements for the successful delivery of AI solutions. Knowledge
7048 Knowledge of the benefits and limitations of AI capabilities. Knowledge
7049 Knowledge of the latest machine learning and AI tools, techniques, and best practices. Knowledge
7050 Knowledge of the nature and function of technology platforms and tools used to create and employ AI. Knowledge
7051 Knowledge of the possible impacts of machine learning blind spots and edge cases. Knowledge
7055 Skill in analyzing the output from machine learning models. Skill
7057 Skill in building and deploying machine learning models. Skill
7059 Skill in creating machine learning models. Skill
7065 Skill in explaining AI concepts and terminology. Skill
7067 Skill in identifying low-probability, high-impact risks in machine learning training data sets. Skill
7069 Skill in identifying risk over the lifespan of an AI solution. Skill
7071 Skill in labeling data to make it more discoverable and understandable. Skill
7075 Skill in testing and evaluating machine learning algorithms or AI solutions. Skill